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TL;DR
Workplace AI use is widespread, but surveys cited by ThorstenMeyerAI.com find that only a small share of companies report substantial profit gains. The findings point to workflow redesign, data readiness and governance—not adoption alone—as key hurdles, while forecasts warn many agent projects may be cancelled.
AI use is spreading across workplaces, but substantial profit gains remain uncommon, according to a November 6 report from ThorstenMeyerAI.com that compares company surveys and industry forecasts. The report cites McKinsey findings that 37 percent of respondents said AI had some effect on their company’s earnings before interest and taxes (EBIT), while only 6 percent met the survey’s definition of high performers, attributing at least 5 percent of EBIT to AI.
The figures come from different studies and measure different things. In the McKinsey 2026 survey of 1,719 respondents, the share reporting some AI effect on EBIT was about unchanged from a year earlier. The report says only 6 percent qualified as high performers. Separately, a Boston Consulting Group study of 1,250 companies found 5 percent were getting value from AI at scale, while 60 percent reported no material value. These are survey results, not audited measures of company-wide returns.
Adoption itself is broader. Eurostat data for 2025 show that 20 percent of EU enterprises with at least 10 employees used AI, up from 13.5 percent. Usage varied by company size: 55 percent among large enterprises and 17 percent among small ones. The source report also cites McKinsey data indicating that 40 percent of companies with more than $1 billion in revenue said they were scaling AI agents, up from 27 percent.
The report defines agents as AI systems that can take steps, such as sorting a case, consulting other systems or drafting a response, rather than only retrieving information or answering a prompt. BCG respondents attributed about 17 percent of their AI value to agents and expected that share to reach 29 percent by 2028. Those figures reflect respondents’ reported value and expectations, not a guaranteed return.
Infographic 01 · Who gets value from AI
Most companies use AI. About 6 percent profit from it in a big way
Adoption keeps rising. The share of companies that can show a large effect on profit has barely moved.
Share of McKinsey’s 2026 respondents who attribute at least 5 percent of their company’s EBIT to AI and call the impact significant. Flat versus 2025, as reported by The Register.
Wide use, thin profit
Different surveys, different bases: read each line with its source
Sources: McKinsey State of AI 2026 (n = 1,719), as reported by The Register, 25 Aug 2026 and TechTimes, 26 Aug 2026 · BCG, The Widening AI Value Gap, Sept 2025 (n = 1,250) · Eurostat, 11 Dec 2025 · Eurostat Statistics Explained (enterprises with 10+ employees). Survey figures are self-reported by executives; Eurostat counts use, not value.
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Why Adoption Has Not Meant Profit
The gap between use and financial results matters because companies may invest in AI without changing the work that determines whether it saves time or money. The report argues that adding a tool to an existing process can leave people doing the same tasks, with AI serving as another layer rather than changing how work moves from start to finish.
For employees, the effects are mixed. The source report cites McKinsey survey respondents who use AI: 80 percent said it made them more productive. At the same time, 39 percent expected job cuts, up from 32 percent in the prior survey. The report says last year’s cuts fell short of what respondents had expected. These are perceptions and survey responses; they do not establish how many jobs AI has caused companies to eliminate.
For leaders, the findings point to a distinction between piloting AI and applying it at scale. BCG’s survey found that leading companies had deployed 62 percent of their AI initiatives, compared with 12 percent among laggards. That association does not, by itself, establish why the leaders performed better. Still, it highlights the practical questions behind the headline figures: which workflows are changing, how results are measured, and who is accountable when an AI system acts.
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From Digitised Forms to Automated Work
Earlier digital transformation programmes often moved paperwork and records into software while leaving people responsible for interpreting each case and deciding what to do next. The report describes AI as extending software into parts of that decision process: a system might read an invoice, compare it with a contract and flag an exception for a person to review. The extent to which a system can act depends on its design, permissions and oversight.
The report draws on several kinds of evidence: Eurostat’s official statistics on AI use in European companies; BCG and McKinsey surveys; Gartner forecasts; and MIT research described in coverage by Fortune. Because the sources use different samples, definitions and measures, their numbers should not be read as a single, directly comparable scorecard. Adoption rates, reported financial effects and forecasts answer different questions.
Barriers appear both technical and organisational. The source says 71 percent of European firms that had considered AI but did not use it cited a lack of expertise. Gartner, as quoted in the report, forecasts that organisations will abandon 60 percent of AI projects that lack AI-ready data. These findings frame data, skills and workflow planning as part of implementation, rather than problems that adoption figures alone can resolve.
How Much Value Companies Actually Gain
The surveys do not establish a common, independently audited measure of AI’s effect on profit. Companies may define AI use, financial impact and “value at scale” differently, and the studies cover distinct groups of respondents. The reported percentages therefore show what participants said under each survey’s definitions, not a definitive comparison of returns across all businesses.
It is also unclear from the source material how much of the reported productivity or EBIT effect can be attributed to AI rather than other changes, or how results differ by industry and individual use case. Gartner’s cancellation estimates and BCG’s expectations for agent value are forecasts, so actual outcomes may differ. The report provides no final evidence on whether projected 2027 cancellations or 2028 value shares will materialise.
Tracking Projects Through 2027
The next test will be whether companies move from trials to sustained use and can document effects on costs, output or earnings. Gartner’s forecast puts a marker at the end of 2027, when it expects more than 40 percent of agentic AI projects to have been cancelled. That prediction will need to be checked against later reporting; it is not a present-day cancellation rate.
For companies adopting agents, the report points to practical steps: select a limited number of workflows, prepare the data and systems they depend on, set permissions and review procedures, and track results against a stated baseline. The studies cited do not prove that these steps will guarantee profit. They do help identify what companies say separates broad experimentation from deployment at scale. Further survey results and company disclosures will show whether reported use translates into measurable financial effects.
Key Questions
How many companies report substantial profit gains from AI?
In the McKinsey 2026 survey cited by the report, 6 percent of respondents’ organisations met its high-performer definition: at least 5 percent of EBIT attributed to AI. This is a survey finding, not an audited figure for all companies.
How common is AI use among EU businesses?
Eurostat reported that 20 percent of EU enterprises with 10 or more employees used AI in 2025, compared with 13.5 percent in 2024. The rate was 55 percent among large enterprises and 17 percent among small ones.
What does the report mean by AI agents?
It describes agents as systems that can take steps toward completing work, such as categorising incoming cases, consulting other systems or drafting a response. What an agent is allowed to do depends on its permissions and human oversight.
Why do some AI projects fail to reach scale?
The report cites cost, unclear value, weak risk controls, missing expertise and unprepared data as barriers. Gartner’s estimates about future cancellations are forecasts, rather than confirmed outcomes.
Do the findings show that AI is already eliminating jobs?
No. The source reports that 39 percent of McKinsey respondents expected job cuts, up from 32 percent, and that earlier cuts were below respondents’ expectations. Those responses do not establish how many job losses were caused by AI.
Source: ThorstenMeyerAI.com
